ArticleInternational journal of particle therapy2019
MRI-Based Proton Treatment Planning for Base of Skull Tumors.
Article in International journal of particle therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Recent advances in applying machine learning to proton radiotherapy.Biomedical physics & engineering express · 2025Pooled it
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy.Frontiers in medicine · 2025Review
- Clinical validation of MR-generated synthetic CT by MRCAT for brain tumor radiotherapy.Journal of applied clinical medical physics · 2025Article
- Clinical implementation of a commercial synthetic computed tomography solution for radiotherapy treatment of glioblastoma.Physics and imaging in radiation oncology · 2024Article
- Review
- Deep neural network-based synthetic image digital fluoroscopy using digitally reconstructed tomography.Physical and engineering sciences in medicine · 2023Article
- Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy.Medical physics · 2023Article
- Possibilities and challenges when using synthetic computed tomography in an adaptive carbon-ion treatment workflow.Zeitschrift fur medizinische Physik · 2023Article
- Article
- An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy.Zeitschrift fur medizinische Physik · 2022Article
- A review of deep learning based methods for medical image multi-organ segmentation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- Learning-Based Stopping Power Mapping on Dual-Energy CT for Proton Radiation Therapy.International journal of particle therapy · 2021Article
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2020Review
- Dosimetric evaluation of synthetic CT generated with GANs for MRI-only proton therapy treatment planning of brain tumors.Journal of applied clinical medical physics · 2020Article
- Feasibility of Magnetic Resonance-based Synthetic Computed Tomography for Proton Dose Calculation in Prostate Cancer.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
purposeTo introduce a novel, deep-learning method to generate synthetic computed tomography (SCT) scans for proton treatment planning and evaluate its efficacy. MATERIALS AND
methods50 Patients with base of skull tumors were divided into 2 nonoverlapping training and study cohorts. Computed tomography and magnetic resonance imaging pairs for patients in the training cohort were used for training our novel 3-dimensional generative adversarial network (cycleGAN) algorithm. Upon completion of the training phase, SCT scans for patients in the study cohort were predicted based on their magnetic resonance images only. The SCT scans obtained were compared against the corresponding original planning computed tomography scans as the ground truth, and mean absolute errors (in Hounsfield units [HU]) and normalized cross-correlations were calculated. Proton plans of 45 Gy in 25 fractions with 2 beams per plan were generated for the patients based on their planning computed tomographies and recalculated on SCT scans. Dose-volume histogram endpoints were compared. A γ-index analysis along 3 cardinal planes intercepting at the isocenter was performed. Proton distal range along each beam was calculated.
resultsImage quality metrics show agreement between the generated SCT scans and the ground truth with mean absolute error values ranging from 38.65 to 65.12 HU and an average of 54.55 ± 6.81 HU and a normalized cross-correlation average of 0.96 ± 0.01. The dosimetric evaluation showed no statistically significant differences (
conclusionsThis study shows our method is capable of generating SCT scans with acceptable image quality, dose distribution agreement, and proton distal range compared with the ground truth. Our results set a promising approach for magnetic resonance imaging-based proton treatment planning.
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